XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples

Cheloni, Stefano; Hillje, Roman; Luzi, Lucilla; Pelicci, Pier Giuseppe; Gatti, Elena

doi:10.1186/s12920-021-00872-8

Cited by 10 publications

(12 citation statements)

References 22 publications

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“…Xenocell version 1.0 (ref. 86 ) was used to classify reads between host (mouse) and graft (human). The bollito pipeline 87 was used to perform read analysis as follows: sequencing quality was checked with FastQC ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ); reads were aligned to the human reference genome (GRCh38_p13 from GENCODE 88 ) with STARsolo (STAR 2.7.3a (ref.…”

Section: Methodsmentioning

confidence: 99%

Stratification of radiosensitive brain metastases based on an actionable S100A9/RAGE resistance mechanism

Monteiro¹,

Miarka²,

Perea-García³

et al. 2022

Nat Med

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Whole-brain radiotherapy (WBRT) is the treatment backbone for many patients with brain metastasis; however, its efficacy in preventing disease progression and the associated toxicity have questioned the clinical impact of this approach and emphasized the need for alternative treatments. Given the limited therapeutic options available for these patients and the poor understanding of the molecular mechanisms underlying the resistance of metastatic lesions to WBRT, we sought to uncover actionable targets and biomarkers that could help to refine patient selection. Through an unbiased analysis of experimental in vivo models of brain metastasis resistant to WBRT, we identified activation of the S100A9–RAGE–NF-κB–JunB pathway in brain metastases as a potential mediator of resistance in this organ. Targeting this pathway genetically or pharmacologically was sufficient to revert the WBRT resistance and increase therapeutic benefits in vivo at lower doses of radiation. In patients with primary melanoma, lung or breast adenocarcinoma developing brain metastasis, endogenous S100A9 levels in brain lesions correlated with clinical response to WBRT and underscored the potential of S100A9 levels in the blood as a noninvasive biomarker. Collectively, we provide a molecular framework to personalize WBRT and improve its efficacy through combination with a radiosensitizer that balances therapeutic benefit and toxicity.

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Section: Methodsmentioning

confidence: 99%

Stratification of radiosensitive brain metastases based on an actionable S100A9/RAGE resistance mechanism

Monteiro¹,

Miarka²,

Perea-García³

et al. 2022

Nat Med

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“…The errors were not identical across tissues or cell types, and some cells had > 10% of reads misaligned to genes of other species. Due to misalignment, cross-species doublet identification via read count or read majority [ 5 , 8 ] may lead to inaccurate calls. In addition, ambient RNA from other species can generate strong false-positive signals, which may disturb downstream analysis.…”

Section: Discussionmentioning

confidence: 99%

Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data

et al. 2022

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Background Although single-cell RNA sequencing of xenograft samples has been widely used, no comprehensive bioinformatics pipeline is available for human and mouse mixed single-cell analyses. Considering the numerous homologous genes across the human and mouse genomes, misalignment errors should be evaluated, and a new algorithm is required. We assessed the extents and effects of misalignment errors and exonic multi-mapping events when using human and mouse combined reference data and developed a new bioinformatics pipeline with expression-based species deconvolution to minimize errors. We also evaluated false-positive signals presumed to originate from ambient RNA of the other species and address the importance to computationally remove them. Result Error when using combined reference account for an average of 0.78% of total reads, but such reads were concentrated to few genes that were greatly affected. Human and mouse mixed single-cell data, analyzed using our pipeline, clustered well with unmixed data and showed higher k-nearest-neighbor batch effect test and Local Inverse Simpson’s Index scores than those derived from Cell Ranger (10 × Genomics). We also applied our pipeline to multispecies multisample single-cell library containing breast cancer xenograft tissue and successfully identified all samples using genomic array and expression. Moreover, diverse cell types in the tumor microenvironment were well captured. Conclusion We present our bioinformatics pipeline for mixed human and mouse single-cell data, which can also be applied to pooled libraries to obtain cost-effective single-cell data. We also address misalignment, multi-mapping error, and ambient RNA as a major consideration points when analyzing multispecies single-cell data.

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“…The error were not identical across tissues or cell types, and few cells had more than 10% of reads misaligned to the other species. Due to misalignment, cross-species doublet identification via read count or by read percentage 5,21 may lead to inaccurate calls. Also ambient RNA from other species generate strong false positive signals and may disturb downstream analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Harmony [30] was used to remove batch effect from mixed and separately sequenced libraries. Cell type was identified by the consensus of scHCL correlation for each cluster.…”

Section: Methodsmentioning

confidence: 99%

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Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data

Choi

Lee

Yoon

et al. 2021

Preprint

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Background: Although single cell RNAseq of xenograft samples are widely used, there is no comprehensive pipeline for human and mouse mixed single cell analysis. Method: We used public data to assess misalignment error when using human and mouse combined reference, and generated a pipeline based on expression-based species deconvolution with species matching reference realignment to remove errors. We also found false-positive signals presumed to originate from ambient RNA of the other species, and use computational method to adequately remove them. Result: Misaligned reads account to on average 0.5% of total reads but expression of few genees were greatly affected leading to 99.8% loss in expression. Human and mouse mixed single cell data analyzed by our pipeline clustered well with unmixed data. We also applied our pipeline to multi-species multi-sample single cell library containing breast cancer xenograft tissue and successfully identified all identities along with the diverse cell types of tumor microenvironment. Conclusion: We present our pipeline for mixed human and mose single cell data which can also be applied to pooled libraries to obtain cost effective single cell data. We also address consideration points when analyzing mixed single cell data for future development.

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XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples

Cited by 10 publications

References 22 publications

Stratification of radiosensitive brain metastases based on an actionable S100A9/RAGE resistance mechanism

Stratification of radiosensitive brain metastases based on an actionable S100A9/RAGE resistance mechanism

Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data

Expression-based species deconvolution and realignment removes misalignment error in multispecies single-cell data

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